28 citations · 30 across the 8 of their papers we have counts for
10 papers
TimeThink: Reasoning with Time for Video LLMs
Handong Li, Longteng Guo, Zikang Liu +8
Video reasoning requires models to identify and verify temporally localized evidence within long video sequences. Recent Video Large Language Models (Video-LLMs) have shown promisi…
Development of Small-pitch, Ultra-thin 3D Silicon Sensors at USTC
Kuo Ma, De Zhang, Shengjia He +13
We report on the development of 3D silicon sensors at the University of Science and Technology of China (USTC). The sensor involves columnar electrodes (5 um in diameter) of both d…
AdaSpark: Adaptive Sparsity for Efficient Long-Video Understanding
Handong Li, Zikang Liu, Longteng Guo +10
Processing long-form videos with Video Large Language Models (Video-LLMs) is computationally prohibitive. Current efficiency methods often compromise fine-grained perception throug…
Thinking in Streaming Video
Zikang Liu, Longteng Guo, Handong Li +7
Real-time understanding of continuous video streams is essential for interactive assistants and multimodal agents operating in dynamic environments. However, most existing video re…
ClinConsensus: A Physician-Calibrated Benchmark for Evaluating Clinical Rubric Coverage in Chinese Medical LLMs
Xiang Zheng, Han Li, Wenjie Luo +15
Open-ended medical LLM evaluation remains weakly grounded in physician-calibrated coverage of clinically relevant response criteria, especially in localized clinical settings. We i…
Breaking the Encoder Barrier for Seamless Video-Language Understanding
Handong Li, Yiyuan Zhang, Longteng Guo +2
Most Video-Large Language Models (Video-LLMs) adopt an encoder-decoder framework, where a vision encoder extracts frame-wise features for processing by a language model. However, t…
Explore the Limits of Omni-modal Pretraining at Scale
Yiyuan Zhang, Handong Li, Jing Liu +1
We propose to build omni-modal intelligence, which is capable of understanding any modality and learning universal representations. In specific, we propose a scalable pretraining p…
COSA: Concatenated Sample Pretrained Vision-Language Foundation Model
Sihan Chen, Xingjian He, Handong Li +3
Due to the limited scale and quality of video-text training corpus, most vision-language foundation models employ image-text datasets for pretraining and primarily focus on modelin…
VAST: A Vision-Audio-Subtitle-Text Omni-Modality Foundation Model and Dataset
Sihan Chen, Handong Li, Qunbo Wang +4
Vision and text have been fully explored in contemporary video-text foundational models, while other modalities such as audio and subtitles in videos have not received sufficient a…
Enhancing Vision-Language Pre-Training with Jointly Learned Questioner and Dense Captioner
Zikang Liu, Sihan Chen, Longteng Guo +3
Large pre-trained multimodal models have demonstrated significant success in a range of downstream tasks, including image captioning, image-text retrieval, visual question answerin…